Modeling Combinatorial Optimization Problems using Electimize

Electimize is a newly-developed evolutionary algorithm (EA) that utilizes the fundamentals of electric conductivity to solve optimization problems. Specifically, Electimize utilizes the relationship between the intensity and resistance (Ohm's law) to evaluate the quality of possible solutions. Electimize was mainly developed to overcome some of the limitations of existing EAs. The advantages of Electimize over other EAs include: 1) its ability to evaluate each value in the solution string independently, which helps in identifying the optimal solution in a relatively shorter duration, and 2) its internal mechanisms, which allow for extensive search of the solution space, identification of alternate optimal solutions, and avoidance of premature convergence. Electimize has been applied successfully to a number of optimization problems in various engineering fields. The results show that Electimize outperforms some other EAs that are commonly used in solving these problems. A main limitation of Electimize is the incorporation of several parameters in the algorithm, which requires a solid understanding of its mechanism in order to effectively use it in optimization. This paper discusses the internal mechanisms and functions of Electimize, the effective initialization of optimization parameters, and how the functions of the algorithm interact together to generate the solution. A detailed step-by-step application example is solved to illustrate the operation of the algorithm in solving combinatorial optimization problems. This should prove useful in exposing the algorithm to researchers and facilitating its use in solving combinatorial optimization problems in other research domains.